This invention relates to pattern recognition systems and, in particular, to pattern recognition systems which employ context and expected values in the recognition process.
In a variety of pattern recognition systems in use today, the inherent or natural context of the patterns being recognized is used to aid the recognition system in making its recognition decision. Patterns are said to have inherent context if there are natural rules or criteria governing the patterns and the interrelationships between patterns or groups of patterns. A particular example of patterns having such inherent context are words and groups of words as in a sentence. In word recognition systems, the semantic and syntactic rules of usage of the language provide inherent context for helping to recognize the individual letters, words and groups of words. In robotic object recognition systems, the natural order of the world provides inherent context. For example, the laws of physics and knowledge about the type of scene (e.g., urban, battlefield, factory, etc.) assist in the recognition of objects and groups of objects. In both cases, the higher-level inherent or natural contextual information can fill in the missing lower level pieces.
While the inherent context of certain patterns has thus proved helpful in recognizing these patterns, there are many types of patterns which do not have any inherent context. For these patterns there has been no use of context in the recognition process. As a result, recognizing these patterns is more difficult and time consuming than would otherwise be the case if context were used.
Digits, alphanumerics or numbers, are types of patterns without natural or inherent context. Thus, in attempting to recognize alphanumerics there is no higher level meaning or set of rules governing the relationship of the individual numbers or sets of numbers which would be useful in the recognition process. Recognition therefore, is more difficult than would otherwise be the case if inherent context were present.
Systems are available, however, for number and other symbol recognition. A particularly advantageous system is the one disclosed in an Article entitled "A Neural Network Model for Selective Attention in Visual Pattern Recognition," written by Kunihiko Fukushima and published in Biological Cybernetics, 55, 5-15 (1986). This article in its entirety is incorporated herein by reference.
As illustrated in FIG. 1 of the Fukushima article, the pattern recognition system of Fukushima is a so-called "hierarchical neural network" having both forward ("afferent") and feedback ("efferent") connections between and within the stages (U.sub.0, U.sub.1, U.sub.2, U.sub.3) of multiple layers of the neural cells (e.g., in the forward path, the cell U.sub.c0 of the stage U.sub.0 and, in the feedback path, the cell W.sub.c0 in the first stage U.sub.0 and the cell W.sub.c3 in the last stage U.sub.3) of the network. These connections are weighted or adjusted by the network during a learning period of the system so that symbols having different essential features result in a maximum output at different output cells (i.e., so called "gnostic cells" U.sub.c3) in the highest level or output stage U.sub.3 of the network. Each symbol is, therefore, categorized by the system according to its essential features. After learning, each gnostic cell of the network is, in turn, associated by the user with some specific category name which is a known common identifier of the symbols whose essential features have been associated with the cell during learning. Thus, for example, where numbers are being recognized, the category names would be the known numbers 0-9 indicative of symbols having the essential features of these known numbers.
The Fukushima network thus learns to categorize presented symbols, i.e., to develop a correspondence between individual symbols and associated output cells. Accordingly, when an unknown pattern (which consists of a sequence of one or more symbols) is presented to the network during its recognition period, the pattern causes the network to develop a sequence of steady states in each of which there is a maximum output at one of the output cells. In each case an unknown symbol is therefore recognized by the network as having the essential symbol features corresponding to this output cell and, therefore, is identifiable by its category name.
During recognition by the Fukushima network, i.e., from presenting a pattern to final steady state (i.e., steady state for the last unrecognized symbol of the pattern), whichever output cell has a maximum output at any given time controls the efferent paths of the network. This is accomplished by use of a maximum detector which compares outputs of the gnostic cells o detect the maximum output. The efferent paths are then controlled by the detector signals via coupling these signals over the signal path X so as to facilitate cells of the network associated with the essential symbol features of the maximum output cell and to inhibit cells of the network which are not associated with these symbol features. The attention of the network thus becomes directed to particular symbol features and this facilitating and inhibiting effect continues until the network is driven to steady state and reaches its decision. During this processing phase, the symbol features upon which attention is focused thus become an integral part of the iterative recognition process.
While not present in the Fukishima system, there are other types of systems which upon completion of the processing phase enter a post-processing phase. In this phase the decision reached by the recognition system is either accepted or rejected in dependence upon comparison of the recognized pattern with an expected pattern. One system of this type has been proposed for reading the digits corresponding to the dollar amounts written on bank checks received in payment of bills. In this system, the previously recognized value is compared with a billed amount.
While the recognition systems of the prior art and, in particular, the Fukushima system, are useable pattern recognition systems, there still is a need for a pattern recognition system which can more quickly and reliably recognize patterns, especially multiple symbol patterns and severely deformed patterns.
It is, therefore, a primary object of the present invention to provide such a pattern recognition system.
It is a further object of the present invention to provide such a pattern recognition system utilizing derived contextual information for the patterns to be recognized.
It is yet a further object of the present invention to provide such a pattern recognition system utilizing expected interpretations of the patterns to be recognized and associated probabilities.
It is still a further object of the present invention to provide such a pattern recognition system for recognizing any symbol and, in particular, typed or hand written numbers.
It is also an object of the present invention to provide such a pattern recognition system for recognizing numbers on checks used in payment of bills.